Search results

Journal Article (7)

  1. 1.
    Journal Article
    Loktyushin, A.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Blind multirigid retrospective motion correction of MR images. Magnetic Resonance in Medicine 73 (4), pp. 1457 - 1468 (2015)
  2. 2.
    Journal Article
    Loktyuschin, A.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Blind Retrospective Motion Correction of MR Images. Magnetic Resonance in Medicine 70 (6), pp. 1608 - 1618 (2013)
  3. 3.
    Journal Article
    Nickisch, H.: glm-ie: Generalised Linear Models Inference Estimation Toolbox. Journal of Machine Learning Research 13, pp. 1699 - 1703 (2012)
  4. 4.
    Journal Article
    Seeger, M.; Nickisch, H.: Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. SIAM Journal on Imaging Sciences 4 (1), pp. 166 - 199 (2011)
  5. 5.
    Journal Article
    Rasmussen, C.; Nickisch, H.: Gaussian Processes for Machine Learning (GPML) Toolbox. The Journal of Machine Learning Research 11, pp. 3011 - 3015 (2010)
  6. 6.
    Journal Article
    Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design. Magnetic Resonance in Medicine 63 (1), pp. 116 - 126 (2010)
  7. 7.
    Journal Article
    Nickisch, H.; Rasmussen, C.: Approximations for Binary Gaussian Process Classification. The Journal of Machine Learning Research 9, pp. 2035 - 2078 (2008)

Conference Paper (8)

  1. 8.
    Conference Paper
    Duvenaud, D.; Nickisch, H.; Rasmussen, C.: Additive Gaussian Processes. In: Advances in Neural Information Processing Systems 24, pp. 226 - 234 (Eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)
  2. 9.
    Conference Paper
    Seeger, M.; Nickisch, H.: Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. In: JMLR Workshop and Conference Proceedings, Vol. 15, pp. 652 - 660 (Eds. Gordon, G.; Dunson, D.; Dudik, M.). 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), Fort Lauderdale, FL, USA, April 11, 2011 - April 13, 2011. MIT Press, Cambridge, MA, USA (2011)
  3. 10.
    Conference Paper
    Nickisch, H.; Rother, C.; Kohli, P.; Rhemann, C.: Learning an interactive segmentation system. In: ICVGIP '10: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, pp. 274 - 281 (Eds. Chellapa, R.; Anandan, P.; Rajagopalan, A.; Narayanan, P.; Torr, P.). Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2010), Chennai, India, December 12, 2010 - December 15, 2010. ACM Press, Nw York, NY, USA (2010)
  4. 11.
    Conference Paper
    Nickisch, H.; Rasmussen, C.: Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. In: DAGM 2010: Pattern Recognition, pp. 271 - 282 (Eds. Goesele, M.; Roth, S.; Kuijper, A.; Schiele, B.; Schindler, K.). 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010), Darmstadt, Germany, September 22, 2010 - September 24, 2010. Springer, Berlin, Germany (2010)
  5. 12.
    Conference Paper
    Lampert, C.; Nickisch, H.; Harmeling, S.: Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 951 - 958. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami Beach, FL, USA, June 20, 2009 - June 25, 2009. IEEE Service Center, Piscataway, NJ, USA (2009)
  6. 13.
    Conference Paper
    Nickisch, H.; Seeger, M.: Convex variational Bayesian inference for large scale generalized linear models. In: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 761 - 768 (Eds. Danyluk, A.; Bottou, L.; Littman, M.). 26th International Conference on Machine Learning, Montreal, Canada, June 14, 2009 - June 18, 2009. ACM Press, New York, NY, USA (2009)
  7. 14.
    Conference Paper
    Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. In: Advances in neural information processing systems 21, pp. 1441 - 1448 (Eds. Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.). Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Vancouver, BC, Canada, December 08, 2008 - December 10, 2008. Curran, Red Hook, NY, USA (2009)
  8. 15.
    Conference Paper
    Seeger, M.; Nickisch, H.: Compressed Sensing and Bayesian Experimental Design. In: ICML '08: Proceedings of the 25th international conference on Machine, pp. 912 - 919 (Eds. Cohen, W.; McCallum, A.; Roweis, S.). 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, July 05, 2008 - July 09, 2008. ACM Press, New York, NY, USA (2008)

Talk (2)

  1. 16.
    Talk
    Loktyushin, A.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Multi-rigid motion correction of MR images. 30th Annual Scientific Meeting ESMRMB 2013, Toulouse, France (2013)
  2. 17.
    Talk
    Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Workshop on Machine Learning: Approaches to Representational Learning and Recognition in Vision. Workshop on Machine Learning: Approaches to Representational Learning and Recognition in Vision, Frankfurt a.M., Germany (2008)

Poster (4)

  1. 18.
    Poster
    Loktyushin, A.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Blind Retrospective Motion Correction of MR Images. 20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), Melbourne, Australia (2012)
  2. 19.
    Poster
    Loktyushin, A.; Nickisch, H.; Pohmann, R.: Retrospective blind motion correction of MR images. 28th Annual Scientific Meeting ESMRMB 2011, Leipzig, Germany (2011)
  3. 20.
    Poster
    Lippert, C.; Stegle, O.; Nickisch, H.; Borgwardt, K.; Weigel, D.: Experimental design for genome-wide association studies. 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2010), Boston, MA, USA (2010)
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